Multi-Agent Reinforcement Learning for Swarm Retrieval with Evolving Neural Network
AffiliationRoyal Academy of Engineering; University of Chester
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AbstractThis research investigates methods for evolving swarm communica-tion in a sim-ulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn phero-mone communication autonomously. Building on previous literature on phero-mone communication, this research applies evolution to adjust the topology and weights of an artificial neural network (ANN) which controls the ant behaviour. Compar-ison of performance is made between a hard-coded benchmark algorithm (BM1), a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was suc-cessfully evolved to achieve the task objective, to collect food and return it to a location.
CitationVaughan, N. (2018) Multi-agent reinforcement learning for swarm retrieval with evolving neural network. In V. Vouloutsi, et al. (Eds.) Biomimetic and Biohybrid Systems 7th International Conference, Living Machines, Paris, France, July 17–20, 2018.
DescriptionThe final publication is available at Springer via https://doi.org/10.1007/978-3-319-95972-6_56
Series/Report no.Lecture Notes in Computer Science, volume 10928
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/3.0/